A financial network perspective of financial institutions’ systemic risk contributions

Abstract

This study considers the effects of the financial institutions’ local topology structure in the financial network on their systemic risk contribution using data from the Chinese stock market. We first measure the systemic risk contribution with the Conditional Value-at-Risk (CoVaR) which is estimated by applying dynamic conditional correlation multivariate GARCH model (DCC-MVGARCH). Financial networks are constructed from dynamic conditional correlations (DCC) with graph filtering method of minimum spanning trees (MSTs). Then we investigate dynamics of systemic risk contributions of financial institution. Also we study dynamics of financial institution’s local topology structure in the financial network. Finally, we analyze the quantitative relationships between the local topology structure and systemic risk contribution with panel data regression analysis. We find that financial institutions with greater node strength, larger node betweenness centrality, larger node closeness centrality and larger node clustering coefficient tend to be associated with larger systemic risk contributions.